According to Haughn and Rouse (2014) the definition of geospatial analysis is:
“Geospatial analysis is the gathering, display, and manipulation of imagery, GPS, satellite photography and historical data, described explicitly in terms of geographic coordinates or implicitly, in terms of a street address, postal code, or forest stand identifier as they are applied to geographic models.”
We did a mapping exercise in class involving data of Oxford Knights which was quite interesting. The data was already gathered by previous students so we could just import the data into alphabetical columns.
By using spatial analysis or datamapping tracking is made easier. By mapping the data with geospatial analysis we were able to see where the Oxford Knights came from to go to school, as well as the differences across the various centuries. Like for example, imagine that in the 1800s most of them came from Wales while in the 1700s they came from somewhere closer to Oxford.
To Geocode this information we used Google Fusion tables. We had to reduce the amount of time it would take to geocode so we did this by setting all the columns in yellow to ‘text’ and then ‘Change’. This meant that the tool ignored those columns for geo-encoding purposes.
As always with manmade things there is a margin for error so for this exercise there was an error that we had to fix. We had to manually change the program’s interpretation of Surrey from a town in Canadas west coast by the same name, and that did not exist at that time, to the correct Surrey in the UK.
When the geo-encoding is done and you get your map the Knights are represented by balloon-like pins in different colours. This is only one of several ways to look at the map. We also used a hotspot map which I thought was easier to read because it was not as spread around, it was easier to interpret and understand. On the heatmap we could increase both radius and opacity to see where these people were clustered.
There really wasn’t any big changes when filtering by year. The biggest difference was probably that more people came from Wales between 1000-1600 than between 1600-1714. By filtering shorter periods like 1550-1570 it was interesting to see that in addition to Oxford there were three other areas that had enough Knights to be significant in the heatmap. However, in the time before the 1550’s there were none or at least not enough Knights to even make a blip on the heatmap. By looking at the map through various filters it seems that the time between 1570-1600 was the peak years of the Oxford Knights. As you can see in the picture below it is clear from the data gathered that the Oxford Knights ended soon after 1710.
When filtering by ‘College’ we see that the graduates are from quite different places. Graduates from Brasenose College come from mid-west around Liverpool, Christ Church College graduates come from southeast in and around London while graduates from Exeter College come from places close to Okehampton and Bodmin which are southwest in England. This tells us that the different colleges attracts students from different areas, possibly because of a wide range of subjects or because of their specific subject.
In conclusion, I don’t think we could have found out everything we did if we hadn’t used geospatial analysis. We might have been able to figure it out, but I am certain that would take a much longer time. I did not know any of this before and I could not tell any of it by looking at the original records. Looking at the original records would mean having to look and compare several of the columns and it would have taken a lot of time. All technology nowadays is to help us make things easier and to go faster, Google Fusion Tables is no exception, which I think is a good thing.